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Sunday, October 29, 2006

loudQUIETloud is a documentary about the Pixies’ reunion tour of 2004. Why do you care?

Maybe you’ve heard of the Pixies and know that they’re somehow important. The nutshell: Late 1980s band more famous after the fact than at the time. Big influence on other bands. (“I was basically trying to rip off the Pixies” —Kurt Cobain on the genesis of “Smells Like Teen Spirit.”)

Maybe you need an antidote to the big-time rockumentary, with its inevitable dramatic arc of obscurity to fame to excess to fall-out to redemption. No big dramatic arc here, more of a constant dilemma: Bandmates don’t get along well, but they need each other to be the Pixies—for the money but also for the meaning, since everything else they do individually is in the Pixies’ shadow.

Maybe you want to relate to rock stars. Now middle-aged and not looking too rock-star-ish, the Pixies are ordinary people with everyday problems. But for a couple hours a night, they become musical superheroes.

Maybe you like the underdog. Bassist Kim Deal’s mother approves of the reunion because it will give her daughter “something to do besides sewing and making snowflakes, crafty stuff.” Prior to the reunion, drummer Dave Lovering was spending a lot of time on the beach with a metal detector. Band leader Charles Thompson (aka Black Francis, aka Frank Black) listens to motivational tapes about being a better person.

Maybe you’re a sucker for the human moments. Charles and family visit the aquarium. Joey watches his new baby grow up via webcam from hotel rooms around the world. Kim reads a fan’s gift: a novel that includes a girl whose hero is Kim Deal.

Sunday, October 22, 2006

Several people have asked what I think of the Netflix Prize, a $1 million contest to improve Netflix’s movie recommendations by 10%. For those expecting an “analyze the analytics” posting like Pandora vs. Last.fm, I’m going to throw you a curveball. I think the more interesting story here is about product marketing—and the Netflix Prize itself is the product.

Productizing a Research Project

From Netflix’s perspective, better recommendations mean higher profits. For those interested in the economics, Chris Anderson (author of The Long Tail) explains them.

But how do you make better recommendations? The usual approach would be to put some researchers on an internal project. Netflix had been doing that for years, but their researchers apparently hit the point of diminishing returns.

Then somebody had the idea of throwing open the problem to the rest of the world, saying something like, “There must be thousands of people with the skills, motivation, and computing hardware to tackle this problem. We just need them to work for us.”

There are indeed many experts in fields like statistical computing, machine learning, and artificial intelligence. There are even more dabblers who know just enough to be dangerous and could come up with answers the pros would never consider. The more people involved, the better the chance of success.

So from Netflix’s perspective, the problem evolved from creating a better algorithm to creating something, the Netflix Prize, that in turn would create Netflix a better algorithm. In essence, they built the Netflix Prize as a product: The “customers” were the prospective researchers; the challenge was to design and market something that would get these customers to participate.

Getting Attention: Eyes on the Prize

The $1 million prize is the most obvious feature. Having noticed the success (and now proliferation) of science-based prizes like the Ansari X Prize, Netflix no doubt liked the combination of free publicity such a prize generates along with the competitive dynamic that real money brings. The press and blogosphere were duly abuzz.

Making It Real: Heavy-Duty Data

Netflix offered up a huge, real-world data set of people’s movie ratings. This alone would have been enough to get lots of smart people playing with the data. Most aspiring data miners—who don’t happen to work at Netflix, Amazon.com, or other data-rich players—rarely if ever get a crack at data like this.

That said, Netflix slightly tainted this feature by “perturbing” an unspecified amount of the data “to prevent certain inferences being drawn about the Netflix customer base.” It’s not a big issue because a built-in limit exists to Netflix’s messing with the data: If the perturbed data ends up differing from the original data in important ways, Netflix could end up with a nightmare scenario where the winning algorithm exploits those differences and thus is not applicable to the original data. If that happened, Netflix would pay $1 million for an algorithm they can’t use on their actual data. As a result, we can safely assume the perturbed data is faithful to the original.

Talking Right: The Web Site

The Netflix Prize has its own Web site with a voice that is well tuned to its “customers,” the researcher types. The Rules and FAQ pages are not written in legalese, academic jargon, or various marketing dialects that no one speaks but that nevertheless appear in written form everywhere. The text is smart but informal, technical where necessary but not gratuitously so. To whomever wrote it, I salute you.

The Web site also includes a simple but effective leaderboard and community forum.

Giving Back: Winner Tells the World

Anticipating that most prospective researchers would immediately look for a catch—like what happens to the intellectual property you submit—Netflix summarizes the relevant terms in plain English: “To win...you must share your method with (and non-exclusively license it to) Netflix, and you must describe to the world how you did it and why it works.” I expected something far more dire. Besides adding a touch of idealism to the proceedings, the bit about telling the world talks to the likeliest suspects for contestants: academics or corporate researchers who have strong professional incentives to publish their work.Selling the Goal: It’s Only 10%“10% improvement” is a clever packaging of the goal, because it’s a lot harder than it sounds. According to the FAQ, Netflix’s own algorithm—the one you’re trying to beat by 10%—is only 10% better than “if you just predicted the average rating for each movie.” In other words, a naive approach works pretty well. And while there is still a significant amount of distance between Netflix’s algorithm and perfection, anything close to perfection is impossible because people are not consistent raters, neither among each other nor individually over time. Thus, a major unknown is how much headroom exists to do better before one hits the wall of rating noise. Yet it is known that achieving the first 10% over a naive approach was far from trivial.

The Results So Far

Three weeks into the competition, more than 10,000 contestants have registered. Twelve contestants have cleared the 1% improvement mark, seven have cleared 2%, three have cleared 3%, and two have cleared 4%. The current leader is at 4.67% improvement, almost half way to the $1 million prize.

Given that Netflix was ready to let the contest run for ten years, and included yearly “Progress Prizes” for contestants that could exceed the best score by 1%, I’d say the Netflix Prize has exceeded expectations so far. And that does not factor-in the positive public relations and consumer awareness that came with the various press hits.

If the progress continues at the current rate, the contest will be over at the three-month minimum that Netflix has set. However, extrapolating from the current pace is risky. Every additional point of improvement will be harder, and we don’t know where the practical limit is.

Why It’s Different

There have been various other data-mining competitions. I’ll hazard a guess that Netflix’s is the first to be covered as a feature story in The New York Times and will easily be the largest ever in term of participation. (The New York Times story is already behind the pay wall, but a syndicated version is available at News.com.)

The comparison with previous competitions is not fair, because other competitions were academic affairs, providing a little collegial competition at conferences. Yet Netflix’s success underlines how much more can be done when a data-mining competition becomes a means to do business.

By treating the Netflix Prize as a product, complete with features designed to maximize “customer” buy-in, Netflix created something far better than spending $1 million on its own researchers’ salaries over time. In that sense, the Netflix Prize is more interesting as a business method—spearheaded by spot-on product marketing—than a “Which algorithm will win?” story.

So I say to Netflix: Great idea, great execution. And to the contestants: May the best algorithm win.

Sunday, October 8, 2006

The U.S. organic food movement started as counterculture but is now accelerating toward the mainstream. Samuel Fromartz’s Organic, Inc. tells the story of how and why.

A business reporter with a soft spot for healthy food, Fromartz pays due respect to both the organic purists, who decry their movement is being sold out to big business, and the organic popularizers like Whole Foods and Earthbound Farms, which have made megabucks spreading the organic gospel far and wide. Along the way, government agencies, agribusiness, and various others players make appearances.

Of the book’s themes, the one I found most interesting was the divergence between healthy food and organic food. In the early years of the organic movement these concepts were nearly synonymous. The goal was food that’s healthy for you and healthy for the planet; organic farming was a key means to the end. However, at the time, whether that healthy food tasted good was a secondary consideration, leading to the societal stereotype of “health food” as bland, killjoy food. But today, people increasingly believe they can have their organic cake and eat it too:

[O]rganic food persisted and grew precisely because the movement defined organic as a production method rather than a prescriptive diet such as Atkins, South Beach, the Zone, or Weight Watchers. The benefit came from eating the food, not from avoiding foods or counting calories. In this way, organic food became associated with a “healthy lifestyle,” which meant you ultimately decided what made you feel good. Whole Foods’s organic chocolate truffles epitomize this for me; they taste good because they contain chocolate, sugar, and saturated fat—not the healthiest mix. Yet by making them organically, Whole Foods tempered the “bad” quotient and transformed them into something “good.”

For the purists, organic chocolate truffles are on the slippery slope that leads to the organic Twinkie, a totemic symbol of the final organic betrayal. Yet for the popularizers, an organic Twinkie is still better, for you and the planet, than a traditional Twinkie.

Adding a twist to this debate, Fromartz notes:

[Organic popularizers] argued that making an organic Twinkie would “Grow the market! Convert more land!” The purists said, “No! Organic food should be kept pure and the Twinkie banned!” What neither side imagined was that consumers might buy conventional Twinkies and wash them down with organic milk, or that such mixed consumption might be preferable.

Per that last quote, Fromartz covers various consumer research that says organic currently is nowhere near an all-or-nothing choice even for price-insensitive people who could buy organic alternatives for most of their food products. Today, people are paying the premium for organic foods selectively, in areas where the benefit is perceived to be most important. For example, organic is particularly strong in baby food, even for lower-income purchasers.

Now, with Wal-Mart looking to drive down organic prices, the further mainstreaming of organic food is inevitable. You may not know it, but healthy-brand icons Odwalla, Boca Burgers, and Kashi are already owned by Coca-Cola, Kraft, and Kellog’s, respectively. And, by the way, not all of these healthy brands’ products are organic—a further reminder that the relationship between “organic” and “healthy” is not simple.

It’s a story with many chapters to play out. Organic, Inc. is a good guide to the action so far.